scholarly journals Forensic Human Identification Using Skin Microbiomes

2017 ◽  
Vol 83 (22) ◽  
Author(s):  
Sarah E. Schmedes ◽  
August E. Woerner ◽  
Bruce Budowle
Keyword(s):  
Supervised Learning ◽  
Classification Accuracy ◽  
Formal Model ◽  
Human Identification ◽  
Attribute Selection ◽  
Skin Microbiome ◽  
Novel Approach ◽  
Long Time ◽  
Three Body ◽  
Microbiome Profiling

ABSTRACT The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification. IMPORTANCE A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.

Population informative markers selected using Wright's fixation index and machine learning improves human identification using the skin microbiome

2021 ◽  
Author(s):  
Allison J. Sherier ◽  
August E. Woerner ◽  
Bruce Budowle
Keyword(s):  
Human Skin ◽  
Human Identification ◽  
Fixation Index ◽  
Support Vector ◽  
Skin Microbiome ◽  
Nucleotide Polymorphisms ◽  
Two Samples ◽  
Biological Evidence ◽  
Microbial Dna ◽  
Microbiome Profiling

Microbial DNA, shed from human skin, can be distinctive to its host and thus help individualize donors of forensic biological evidence. Previous studies have utilized single locus microbial DNA markers (e.g., 16S rRNA) to assess the presence/absence of personal microbiota to profile human hosts. However, since the taxonomic composition of the microbiome is in constant fluctuation, this approach may not be sufficiently robust for human identification (HID). Multi-marker approaches may be more powerful. Additionally, genetic differentiation, rather than taxonomic distinction, may be more individualizing. To this end, the non-dominant hands of 51 individuals were sampled in triplicate (n = 153). They were analyzed for markers in the hidSkinPlex, a multiplex panel comprising candidate markers for skin microbiome profiling. Single nucleotide polymorphisms (SNPs) with the highest Wright’s fixation index (F ST ) estimates were then selected for predicting donor identity using a support vector machine (SVM) learning model. F ST is an estimate of the genetic differences within and between populations. Three different SNP selection criteria were employed: SNPs with the highest-ranking F ST estimates 1) common between any two samples regardless of markers present (termed overall ); 2) each marker common between samples (termed per marker ); and 3) common to all samples used to train the SVM algorithm for HID (termed selected ). The SNPs chosen based on criteria for overall , per marker, and selected methods resulted in an accuracy of 92.00%, 94.77%, and 88.00%, respectively. The results support that estimates of F ST , combined with SVM, can notably improve forensic HID via skin microbiome profiling. IMPORTANCE There is a need for additional genetic information to help identify the source of biological evidence found at a crime scene. The human skin microbiome is a potentially abundant source of DNA that can enable the identification of a donor of biological evidence. With microbial profiling for human identification, there will be an additional source of DNA to identify individuals as well as to exclude individuals wrongly associated with biological evidence, thereby improving the utility of forensic DNA profiling to support criminal investigations.

scholarly journals Skin Microbiome Analysis for Forensic Human Identification: What Do We Know So Far?

2020 ◽  
Vol 8 (6) ◽  
pp. 873 ◽  
Author(s):  
Pamela Tozzo ◽  
Gabriella D’Angiolella ◽  
Paola Brun ◽  
Ignazio Castagliuolo ◽  
Sarah Gino ◽  
...  
Keyword(s):  
Human Identification ◽  
Spatial And Temporal Variability ◽  
Skin Microbiome ◽  
Forensic Evidence ◽  
Microbiome Analysis ◽  
Wide Range ◽  
Microbiome Research ◽  
Meta Analyses ◽  
High Degree ◽  
Microbiome Profiling

Microbiome research is a highly transdisciplinary field with a wide range of applications and methods for studying it, involving different computational approaches and models. The fact that different people host radically different microbiota highlights forensic perspectives in understanding what leads to this variation and what regulates it, in order to effectively use microbes as forensic evidence. This narrative review provides an overview of some of the main scientific works so far produced, focusing on the potentiality of using skin microbiome profiling for human identification in forensics. This review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The examined literature clearly ascertains that skin microbial communities, although personalized, vary systematically across body sites and time, with intrapersonal differences over time smaller than interpersonal ones, showing such a high degree of spatial and temporal variability that the degree and nature of this variability can constitute in itself an important parameter useful in distinguishing individuals from one another. Even making the effort to organically synthesize all results achieved until now, it is quite evident that these results are still the pieces of a puzzle, which is not yet complete.

Deuteronomy 14.3–21: An Early Exemplar of Rewritten Scripture?

2021 ◽  
Vol 45 (3) ◽  
pp. 422-457
Author(s):  
A. Friedberg ◽  
Juni Hoppe
Keyword(s):  
Second Temple ◽  
Novel Approach ◽  
Long Time ◽  
Dietary Laws

The almost verbatim parallels of the dietary laws in Lev. 11 and Deut. 14 have baffled scholars for a long time. We reexamine the evidence, offer a novel approach to determining the direction of dependency, and point out the notable similarities the borrowing bears to Second Temple editorial and redactional practices, drawing on recent Qumran scholarship. We conclude that Deut. 14.3–21 may be one of the earliest specimens of Rewritten Scripture.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

Algorithmic music generation by harmony recombination with genetic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Omar Lopez-Rincon ◽  
Oleg Starostenko ◽  
Alejandro Lopez-Rincon
Keyword(s):  
Genetic Algorithm ◽  
New Music ◽  
Novel Approach ◽  
Time Patterns ◽  
Algorithmic Music ◽  
Long Time ◽  
Music Generation ◽  
Music Piece ◽  
Short Time ◽  
Algorithmic Music Composition

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.

scholarly journals Integrating public risk perception into formal natural hazard risk assessment

2006 ◽  
Vol 6 (3) ◽  
pp. 471-483 ◽  
Author(s):  
Th. Plattner ◽  
T. Plapp ◽  
B. Hebel
Keyword(s):  
Risk Assessment ◽  
Risk Perception ◽  
Risk Evaluation ◽  
Formal Model ◽  
Relevant Literature ◽  
Historical Context ◽  
Individual Risk ◽  
Affecting Factors ◽  
Plausibility Check ◽  
Novel Approach

Abstract. An urgent need to take perception into account for risk assessment has been pointed out by relevant literature, its impact in terms of risk-related behaviour by individuals is obvious. This study represents an effort to overcome the broadly discussed question of whether risk perception is quantifiable or not by proposing a still simple but applicable methodology. A novel approach is elaborated to obtain a more accurate and comprehensive quantification of risk in comparison to present formal risk evaluation practice. A consideration of relevant factors enables a explicit quantification of individual risk perception and evaluation. The model approach integrates the effective individual risk reff and a weighted mean of relevant perception affecting factors PAF. The relevant PAF cover voluntariness of risk-taking, individual reducibility of risk, knowledge and experience, endangerment, subjective damage rating and subjective recurrence frequency perception. The approach assigns an individual weight to each PAF to represent its impact magnitude. The quantification of these weights is target-group-dependent (e.g. experts, laypersons) and may be effected by psychometric methods. The novel approach is subject to a plausibility check using data from an expert-workshop. A first model application is conducted by means of data of an empirical risk perception study in Western Germany to deduce PAF and weight quantification as well as to confirm and evaluate model applicbility and flexibility. Main fields of application will be a quantification of risk perception by individual persons in a formal and technical way e.g. for the purpose of risk communication issues in illustrating differing perspectives of experts and non-experts. For decision making processes this model will have to be applied with caution, since it is by definition not designed to quantify risk acceptance or risk evaluation. The approach may well explain how risk perception differs, but not why it differs. The formal model generates only "snap shots" and considers neither the socio-cultural nor the historical context of risk perception, since it is a highly individualistic and non-contextual approach.

scholarly journals Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

2020 ◽  
Vol 12 (19) ◽  
pp. 3202
Author(s):  
Xinran Chen ◽  
Yulin Zhan ◽  
Yan Liu ◽  
Xingfa Gu ◽  
Tao Yu ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Temperature Index ◽  
Long Time Series ◽  
Ndvi Time Series ◽  
Long Time

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

scholarly journals A Novel Approach of Parallel Retina-Like Computational Ghost Imaging

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7093
Author(s):  
Jie Cao ◽  
Dong Zhou ◽  
Fanghua Zhang ◽  
Huan Cui ◽  
Yingqiang Zhang ◽  
...  
Keyword(s):  
Wide Spectrum ◽  
Low Cost ◽  
Parallel Architecture ◽  
Structure Design ◽  
Ghost Imaging ◽  
Novel Approach ◽  
Time Correlations ◽  
Long Time ◽  
Sampling And Reconstruction ◽  
Similar Imaging

Computational ghost imaging (CGI), with the advantages of wide spectrum, low cost, and robustness to light scattering, has been widely used in many applications. The key issue is long time correlations for acceptable imaging quality. To overcome the issue, we propose parallel retina-like computational ghost imaging (PRGI) method to improve the performance of CGI. In the PRGI scheme, sampling and reconstruction are carried out by using the patterns which are divided into blocks from designed retina-like patterns. Then, the reconstructed image of each block is stitched into the entire image corresponding to the object. The simulations demonstrate that the proposed PRGI method can obtain a sharper image while greatly reducing the time cost than CGI based on compressive sensing (CSGI), parallel architecture (PGI), and retina-like structure (RGI), thereby improving the performance of CGI. The proposed method with reasonable structure design and variable selection may lead to improve performance for similar imaging methods and provide a novel technique for real-time imaging applications.

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